Abstract:
Systems and methods are provided for generating an image-based color palette based on a color image. A color palette can be a collection of representative colors each associated with a weight or other metadata. A color palette may be generated based on palette generation criteria, which may facilitate or control a palette generation process. Illustratively, the palette generation process may include image pre-processing, color distribution generation, representative color identification, palette candidate generation and palette determination. Representative colors with associated weight can be identified from a distribution of colors depicted by the color image, multiple palette candidates corresponding to the same color image can be generated based on various palette generation criteria, and a color palette can be identified therefrom.
Abstract:
Systems and methods are provided for generating an image-based color palette based on a color image. A color palette can be a collection of representative colors each associated with a weight or other metadata. A color palette may be generated based on palette generation criteria, which may facilitate or control a palette generation process. Illustratively, the palette generation process may include image pre-processing, color distribution generation, representative color identification, palette candidate generation and palette determination. Representative colors with associated weight can be identified from a distribution of colors depicted by the color image, multiple palette candidates corresponding to the same color image can be generated based on various palette generation criteria, and a color palette can be identified therefrom.
Abstract:
Systems and methods are provided for generating an image-based color palette based on a color image. A color palette can be a collection of representative colors each associated with a weight or other metadata. A color palette may be generated based on palette generation criteria, which may facilitate or control a palette generation process. Illustratively, the palette generation process may include image pre-processing, color distribution generation, representative color identification, palette candidate generation and palette determination. Representative colors with associated weight can be identified from a distribution of colors depicted by the color image, multiple palette candidates corresponding to the same color image can be generated based on various palette generation criteria, and a color palette can be identified therefrom.
Abstract:
Systems and methods are described that recommend images, items, and/or metadata based at least in part on a reference color palette or reference color name. A color name can be converted into a representation of the color name in a color space. The reference color can be used to identify images that contain the reference color. The identified images and associated metadata can be analyzed, sorted and provided as an ordered list of items. Systems and methods are also described that identify items that contain colors affiliated with the reference color. Systems and methods are also described that validate color identifier information in metadata associated with an image. Systems and methods are also described that identify non-color specific keywords associated with the reference color.
Abstract:
Systems and methods are provided for generating color names for colors corresponding to images and/or palettes. A color image is obtained, and one or more color palettes corresponding to the color image are identified. The color palette may be generated based on palette generation criteria, which may facilitate or control a palette generation process. Illustratively, the palette generation process may include image pre-processing, color distribution generation, representative color identification, palette candidate generation, and palette determination. A color name for each color identified in the color palette and/or the color image can be identified based at least in part on color name popularity information. Color name popularity information may be identified from color name-related voting results provided by a social network site. Aspects of the disclosure are further directed to processing the identified color name(s), such as updating color name metadata associated with the original color image and/or the color palette.
Abstract:
Systems and methods are provided for generating social networking recommendations. A color preference of a first user may be determined from a color palette of a first image associated with the user and/or a color palette of an item associated with the user. Other users may be identified that have a similar color preference as the first user based at least in part on the determined color preference of the first user. Interactions between the first user and one or more other users having similar color preferences with respect to the first user may be facilitated. A social networking recommendation may be generated with respect to the one or more other users having similar color preferences with respect to the first user.
Abstract:
Systems and methods are provided for generating color names for colors corresponding to images and/or palettes. A color image is obtained, and one or more color palettes corresponding to the color image are identified. The color palette may be generated based on palette generation criteria, which may facilitate or control a palette generation process. Illustratively, the palette generation process may include image pre-processing, color distribution generation, representative color identification, palette candidate generation, and palette determination. A color name for each color identified in the color palette and/or the color image can be identified based at least in part on color name popularity information. Color name popularity information may be identified from color name-related voting results provided by a social network site. Aspects of the disclosure are further directed to processing the identified color name(s), such as updating color name metadata associated with the original color image and/or the color palette.
Abstract:
Systems and methods are provided for generating social networking recommendations. A color preference of a first user may be determined from a color palette of a first image associated with the user and/or a color palette of an item associated with the user. Other users may be identified that have a similar color preference as the first user based at least in part on the determined color preference of the first user. Interactions between the first user and one or more other users having similar color preferences with respect to the first user may be facilitated. A social networking recommendation may be generated with respect to the one or more other users having similar color preferences with respect to the first user.
Abstract:
Techniques for providing instruction to devices in the network are provided. For example, based at least in part on the list of currently active devices and the command, a controller can determine a sequence of IR signals associated with at least one currently active device and transmit, utilizing an IR transmitter incorporated with the controller, the sequence of IR signals to one or more of the currently active devices. In another example, an audio command and location information can be received and used to determine where a controller should transmit an IR signal that corresponds with the audio command, based at least in part on the location information.
Abstract:
Techniques for curating audio and IR commands using machine learning may be provided. For example, the system can receive an audio stream that includes a plurality of audio segments and the system can store the audio stream and/or segments. The system can also store a command in a second data store. When a portion of the audio is provided in conjunction with a same command around the same time and exceeds a threshold number of repetitions, the next time that that audio segment is received, the system may provide a command that corresponds with that audio segment to an output device to cause an operation of the output device. In some examples, the system may confirm that the audio segment corresponds with the command before providing the command. This disclosure may use, for example, signal detection, acoustic fingerprinting, and shared vocabulary lists.